Biophysical microenvironmental factors have shown to couple and interact to regulate various cell behaviors. Although plenty of phenomenological work has been done previously, a deep and comprehensive understanding of how biophysical conditions affect the cellular transcriptome is lacking. In this study, I used RNA sequencing and data analysis to screen the response of the cellular transcriptome of mouse mesenchymal stem cells (mMSCs) to different combinations of biophysical cues.

mMSCs were encapsulated in eight conditions: low or high values of stiffness, stress relaxation, and ligand density. Using computational packages from the Galaxy platform, I preprocessed the sequences and performed differential gene expression analysis for each pairwise conditions. Principal component analysis plots, sample-to-sample distances, MA plots, and p-value histograms were used to visualize the separation of clusters and the distribution of gene counts. Overall, I observed a large discrepancy in the number of differentially expressed (DE) genes and their regulations under different parameter conditions. The number of DE genes heatmap demonstrated the context-dependence of biophysical sensing, where certain matrix properties can modulate the response of cells to other matrix properties. Functional enrichment analysis by g:Profiler revealed the biological pathways related to the most significantly DE genes. The results generated from this study could inform future hypotheses linking cell function to microenvironmental biophysical cues.

Team Members

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Project Mentors, Sponsors, and Partners

• Johns Hopkins University Whiting School of Engineering

• Johns Hopkins Institute for Nanobiotechnology

• Dr. Luo Gu

• Zhiwei Fang

Course Faculty

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